# import keras
# from keras.datasets import boston_housing
# from keras.models import Sequential
# from keras.layers import Dense
# from tensorflow.keras.optimizers import RMSprop
# from keras.callbacks import EarlyStopping
# from sklearn import preprocessing
# from sklearn.preprocessing import scale
#
# (x_train, y_train), (x_test, y_test) = boston_housing.load_data()
#
# x_train_scaled = preprocessing.scale(x_train)
# scaler = preprocessing.StandardScaler().fit(x_train)
# x_test_scaled = scaler.transform(x_test)
#
# model = Sequential()
# model.add(Dense(64, kernel_initializer='normal', activation='relu', input_shape=(13,)))
# model.add(Dense(64, activation='relu'))
# model.add(Dense(1))
#
# model.compile(
#     loss='mse',
#     optimizer=RMSprop(),
#     metrics=['mean_absolute_error']
# )
#
# history = model.fit(
#     x_train_scaled, y_train,
#     batch_size=128,
#     epochs=200,
#     verbose=1,
#     validation_split=0.2,
#     callbacks=[EarlyStopping(monitor='val_loss', patience=20)]
# )
#
#
# score = model.evaluate(x_test_scaled, y_test, verbose = 0)
# print('Test loss:', score[0])
# print('Test accuracy:', score[1])
#
#
# prediction = model.predict(x_test_scaled)
# print(prediction.flatten())
# print(y_test)